ai-html-parse 0.1.0

AI HTML Parser

AI-Powered HTML Parser

Installation

Prerequisites

• Python 3.8 or higher
• Required Libraries:
  • requests
  • bs4 (BeautifulSoup)

Steps

1. Clone the repository:

   git clone https://github.com/pythonshik/ai-html-parser.git
   cd ai-html-parser
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up your API key for Google Gemini:
   • Create a folder named AI in the root directory.
   • Add your API key to a file named gemini_api_key inside the AI folder.

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Usage

Example

1. Import the AIparser class:

   from AIparse import AIparser
   

2. Initialize the parser with a URL:

   element = AIparser("https://www.youtube.com/@PythonShik")
   

3. Parse specific elements:

   for i in ["number of videos", "number of subscribers"]:
       parsed_data = element.parse(i)
       print(f"{parsed_data['explain']}: {parsed_data['value']}")
   

4. Output example:

   {
     "value": "96",
     "explain": "Number of subscribers",
     "result": "96 subscribers"
   }
   

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Overview

This project is an AI-powered HTML parser designed to extract specific data from web pages using Google Gemini's text generation API. The parser processes the HTML source code of a webpage, identifies specific elements, and returns the desired information in a structured JSON format.

Key Features

• AI Integration: Utilizes Google Gemini for intelligent text analysis.
• HTML Parsing: Extracts and processes HTML elements using BeautifulSoup.
• Customizable Instructions: Supports user-defined parsing instructions.
• JSON Output: Provides clear and structured results in JSON format.

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How It Works

1. User Input: Provide a URL and the target element to parse.
2. HTML Fetching: The tool fetches the HTML source code of the webpage.
3. AI Analysis: The HTML source and target element are sent to the AI for processing.
4. JSON Output: The AI generates a structured response containing the extracted information.

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File Descriptions

1. BASE.py

The core class for interacting with Google Gemini's text generation API.

• Features:
  • API key management.
  • Methods for adding and managing conversation history.
  • Text generation using the generate() method.
• Key Methods:
  • history_add(role, content): Adds messages to the conversation history.
  • generate(): Sends data to gemini API and retrieves the generated text.
  • export_history(filename): Saves conversation history to a file.
  • import_history(filename): Loads conversation history from a file.
  • clear_history(filename): Clears the conversation history.

2. prompts.py

Defines the instruction format for AI tasks.

• Key Class: Instructions
  • first_instruction: Provides a detailed guide for parsing HTML elements and formatting the response.

3. main.py

The main entry point for the application.

• Features:
  • Manages the parsing process using AIparser.
  • Configures and interacts with the Gen class for AI communication.
  • Outputs results for specific elements like "number of subscribers" or "number of videos".
• Key Methods:
  • AIparser.__init__: Initializes the parser with a URL and target element.
  • AIparser.parse(element): Parses the given element and retrieves AI-generated results.

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Target Audience

This tool is ideal for:

• Marketers and Analysts: For monitoring trends, gathering competitor data, and extracting insights.
• Small and Medium Businesses: To automate tasks like market monitoring or customer review aggregation.
• SEO Specialists: To analyze site content, keywords, and metadata.
• Developers and Freelancers: To speed up the execution of client parsing tasks.
• Journalists and Bloggers: To gather data for articles and posts effortlessly.

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Limitations

• Speed: Processing time can take up to 45 seconds due to the AI generation.
• Dependencies: Requires an active internet connection and a valid API key.
• Scalability: Not optimized for high-frequency requests.

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Potential Use Cases

• Monitoring changes on web pages.
• Extracting market research data.
• Analyzing competitors' content.
• Automating reporting tasks.

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Future Improvements

• Optimize performance with batch processing and caching.
• Add support for local AI models to reduce dependency on external APIs.
• Expand parsing capabilities to include other data formats like JSON and XML.
• Develop a user-friendly interface (e.g., Telegram bot or web app).

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Contributing

Feel free to contribute to the project by submitting issues or pull requests.

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License

This project is licensed under the MIT License.